The prevalence of carotid subclinical atherosclerosis according to age: A systematic review and meta-analysis of the young to middle-age
Bibliographic record
Abstract
BACKGROUD: As the burden of atherosclerotic cardiovascular disease (ASCVD) continues to rise, knowing the prevalence of its subclinical form is essential for early risk stratification and prevention. This review investigated the prevalence of carotid subclinical atherosclerosis, defined by the presence of plaque detected by ultrasound of the carotid arteries, as it varies with age in the asymptomatic young to middle-aged general population. SOURCES OF MATERIAL: We searched MEDLINE, EMBASE, PubMed, and CENTRAL (2005-2023) databases for observational and experimental studies assessing a population of asymptomatic (ie, no known cardiovascular disease), individuals (aged 18-65 years) who underwent carotid artery ultrasound imaging. Prevalence of plaque was calculated from each included study, as well as based on age and sex subgroups, and Freeman-Tukey double-arcsine-transformed prevalence measures were computed for each study. Pooled measures were generated with a random-effects meta-analysis model. ABSTRACT OF FINDINGS: A total of 92 studies comprising 204,997 subjects were included in this review (median sample size: 859). The median age of the study sample was 49.5 years (range: 26.6-64 years). The overall pooled prevalence of carotid atherosclerosis across all studies was 23% (95% CI: 19.1%-27.1%). The prevalence of carotid plaque increased according to age, ranging from a pooled prevalence of 5.2% (95% CI: 2.4%-8.9%) in adults aged 18 to 45 years to 30.3% (95% CI: 21.8%-39.4%) in those aged 50 to 65 years. CONCLUSION: Subclinical carotid atherosclerosis is prevalent among young to middle-aged healthy adults, free of known CVD, from the general population. The findings suggest that a significant proportion of low-risk individuals may have undetected plaque, justifying reconsideration of early screening strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".